We evaluate the performance of different methods in terms of their accuracy and fairness.
On the Adult dataset, we compare three widely used methods for feature based representation learning, including logistic regression (LR), deep neural network (DNN), and factorization machine (FM).For logistic regression, we regard the element-wise multiplication between weights and input feature vectors as the hidden representations.
For the DNN based methods, we use two hidden layers with ReLU activation functions.
On the basis of these methods, we further compare five methods, including: (1) adversarial learning (denoted as AL), which applies adversarial learning to the representations learned by the model;
(2) adversarial learning with self-training (denoted as AL+ST), which uses an attribute predictor to predict sensitive attributes of samples without attribute labels and add confident predictions to the training set [1]}, [2]};
(3) decomposed adversarial learning (denoted as DAL) [3]}, which is the basic framework in our method;
(4) decomposed adversarial learning with self-training (denoted as DAL+ST);
(5) Semi-FairVAE, our proposed semi-supervised adversarial learning method.
On the NewsRec dataset, we use three widely compared benchmark baselines as basic models, including NAML [4]}, LSTUR [5]} and NRMS [6]}.
In these methods, candidate news articles in an impression are ranked by their personalized click scores given a target user.
We also compare five methods based on them, including (1) AL, vanilla adversarial learning; (2) AL+ST, adversarial learning with self-training; (3) FairRec [3]}, decomposed adversarial learning for news recommendation; (4) FairRec+ST, combining FairRec with self-training; (5) Semi-FairVAE, our approach.
We compare model performance and fairness under different amounts of data (i.e., 10%, 20% and 50%) with sensitive attribute labels.
The samples without attribute labels are ignored in adversarial training, but they still participate in task label prediction and input data reconstruction.
The results are shown in Tables REF  and REF , from which we have the following observations:
